Les aires cérébrales entretiennent des relations fonctionnelles, formant ainsi des réseaux qui peuvent être altérés dans diverses pathologies. L'étude de ces réseaux de connectivité fonctionnelle pourrait potentiellement aider au diagnostic d'un individu et au traitement de sa pathologie. À travers quatre études, nous avons montré que l'analyse en composantes indépendantes spatiale est une méthode suffisamment sensible, reproductible et spécifique pour mettre en évidence, à l’échelle individuelle et au repos, des réseaux sains et pathologiques fiables. Ainsi, le réseau pathologique sous-tendant les hallucinations acoustico-verbales permet de définir les aires cérébrales à traiter par stimulation magnétique transcrânienne. Parmi les réseaux sains, ceux qui impliquent le cortex cingulaire postérieur et le précunéus semblent profondément altérés dans les troubles de la conscience, et peuvent servir d'outil diagnostic pour distinguer le locked-in syndrome de l'état végétatif. Il est désormais possible de cartographier, à l'échelle individuelle, les relations fonctionnelles entre les aires cérébrales. L’étude à venir de la dynamique et du niveau d’activité des réseaux de connectivité fonctionnelle nous renseignera davantage sur leurs fonctions et leur implication dans la pathologie. / Brain areas are functionally connected in networks, even at rest. Since such connectivity networks could be impaired in several pathologies, they could potentially serve for diagnosis and treatment. Based on four studies, spatial independent component analysis has shown sufficient sensitivity, reproducibility and specificity to produce reliable healthy as well as pathological networks at the individual level. Therefore, the network underlying auditory hallucination could define the brain areas to treat by transcranial magnetic stimulation. Among the common resting-state networks, the ones involving the posterior cingular cortex and the precuneus seem deeply altered in disorders of consciousness, and so could be used as a diagnostic tool to distinguish the locked-in syndrome from the vegetative state. We can now map, at the individual level, the functional relationships between brain areas. Further studies on the dynamic and on the level of activity of the functional connectivity networks might provide relevant information about their functions and their involvement in the pathology.

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

The objective of the present thesis research is to apply state-of-the-art experimental and data analysis techniques to the study of gas-liquid pipe flows, with a focus on conditions occurring in header-feeder systems of nuclear reactors under different accident scenarios. Novel experimental techniques have been proposed for the identification of the flow regime and measurement of the flow rates of both phases in gas-liquid flows. These techniques were automated, non-intrusive and economical, which ensured that their use would be feasible in industrial as well as laboratory settings. Measurements of differential pressure and the gas and liquid flow rates were collected in vertical upwards air-water flow at near-atmospheric pressure. It was demonstrated that the probability density function of the normalized differential pressure was indicative of the flow regime and using non-linear dimensionality reduction (the Elastic Maps Algorithm), it was possible to automate the process of identifying the flow regime from the differential pressure signal. The relationship between the probability density function and the power spectral density of normalized differential pressure with the gas and liquid flow rates in air-water pipe flow was also established and a machine learning algorithm (using Independent Component Analysis and Artificial Neural Networks) was proposed for the estimation of the phase flow rates from these properties. The proposed methods were adapted for use with single and dual conductivity wire-mesh sensors in vertical upwards and downwards air--water flows. A thorough evaluation of the performance and measurement uncertainty of wire-mesh sensors in gas-liquid flows was also performed. Lastly, measurements of the flow distribution in feeder tubes supplied with air-water mixtures by a simplified header model were collected and correlated to the observed flow patterns in the header.

Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.

This thesis deals with multi-channel methods of speech enhancement. Multichannel methods of speech enhancement use a few microphones for recording signals. From mixtures of signals, for example, individual speakers can be separated, noise should be reduced etc. with using neural networks. The task of separating speakers is known as a cocktail-party effect. The main method of solving this problem is called independent component analysis. At first there are described its theoretical foundation and presented conditions and requirements for its application. Methods of ICA try to separate the mixtures with help of searching the minimal gaussian properties of signals. For the analysis of independent components are used different mathematical properties of signals such as kurtosis and entropy. Signals, which were mixed artificially on a computer, can be relatively well separated using, for example, FastICA algorithm or ICA gradient ascent. However, difficult is situation, if we want to separate the signals created in the real recording enviroment, because the separation of speech people speaking at the same time in the real environment affects other various factors such as acoustic properties of the room, noise, delays, reflections from the walls, the position or the type of microphones, etc. Work presents aproach of independent component analysis in the frequency domain, which can successfully separate also recordings made in the real environment.

No description available.

Encoding and consolidation of memories are two processes within the hippocampus, and connected cortical networks, that recruit different circuit level dynamics to effectively process and pass information from brain region to brain region. In the hippocampal CA1 pyramidal layer local field potential (LFP), these processes take the form of theta and sharp wave ripples (SPW-Rs) for encoding and consolidation, respectively. As an animal runs through an environment, neurons become active at specific locations in the environment (place cells) increasing their firing rate, functionally representing these specific locations. These firing rate increases are organized within the local theta oscillations and sequential activation of many place cells creates a map of the environment. Once the animal stops moving and begins consummatory behaviors, such as eating, drinking, or grooming, theta activity diminishes, and large irregular activity (LIA) begins to dominate the LFP. Spontaneously, with the LIA, the place cells active during the experience are replayed during SPW-Rs in the same spatial order they were encountered in the environment. Both theta and SPW-R oscillations and their associated neuronal firing are necessary for effective place recognition as well as learning and memory. As such, interruption or termination of SPW-R events results in decreased learning performance over days. During exploration, the associated theta and sequential place cell activity is thought to encode the experience. During quiet restfulness or slow wave sleep (SWS), SPW-R events, that replay experience specific place sequences, are thought to be the signal by which systems consolidation progresses and the hippocampus guides cortical synaptic reorganization. The granular retrosplenial cortex (gRSC) is an associational area that exhibits high frequency oscillations (HFOs) during both hippocampal theta and SPW-Rs, and is potentially a period when the gRSC interprets incoming content from the hippocampus during encoding and systems consolidation. However, the precise laminar organization of synaptic currents supporting HFOs, whether the local gRSC circuitry can support HFOs without patterned input, and the precise coupling of hippocmapla oscillations to gRSC HFOs across brain states remains unknown. We aimed to answer these questions using in vivo, awake electrophysiological recordings in head-fixed mice that were trained to run for water rewards in a 1D virtual environment. We show that gRSC synaptic currents supporting HFOs, across all awake brain states, are exclusively localized to layer 2/3 (L2/3), even when events are detected within layer 5 (L5). Using focal optogenetics, both L2/3 and L5 can generate induced HFOs given a strong enough broad stimulation. Spontaneous gRSC HFOs occurring outside of SPW-Rs are highly comodulated with medial entorhinal cortex (MEC) generated high gamma in hippocampal stratum lacunosum moleculare. gRSC HFOs may serve a necessary role in communication between the hippocampus during SPW-Rs states and between the hippocampus, gRSC, and MEC during theta states to support memory consolidation and memory encoding, respectively. / Doctor of Philosophy / As an animal moves through an environment, individual neurons in the hippocampus, known as place cells, increase and decrease their firing rate as the animal enters and exits specific locations in the environment. Within an environment, multiple neurons become active in different locations, this cooperation of spiking in various locations creates a place map of the environment. Now let's say when the animal moved from one corner of the environment to another, place cells 'A', 'C', 'B', 'E', and 'D' became active in that order. This means, at any given point in the environment, the animal is standing in a venn-diagram-esque overlap of place fields, or locations individual place cells represent. A key question that entranced researchers for many years was how do these neurons know when to be active to not impinge on their neighbor's locations? The answer to this question rested with population electrical activity, known as the local field potential (LFP), that place cell activity is paced to. During active navigation through an environment, place cells activity is coupled to the phase of a slow ~8 hertz (Hz) theta oscillation. Within one theta cycle, or peak to peak, multiple place cells are active, representing the venn diagram of location the animal is in. Importantly, this theta activity and encoding of place cell activity is largely seen during active running or rapid eye movement (REM) sleep. During slow wave sleep (SWS), after an animal has experienced a specific environment and has created a place map, place cells are reactivated in the same order the animal experienced them in. From our previous example, the content of this reactivation would be the place cells 'A', 'C', 'B', 'E', and 'D' which all would be reactivated in that same order. These reactivations or replays occur during highly synchronous and fast LFP oscillations known as sharp wave-ripples (SPW-Rs). SPW-Rs are thought to be a key LFP event that drives memory consolidation and the eventual conversion of short-term memory into long-term memory. However, for consolidation to occur, connected cortical regions need to be able to receive and interpret the information within SPW-Rs. The granular retrosplenial cortex (gRSC) is one proposed region that serves this role. During SPW-Rs the superficial gRSC has been shown to exhibit high frequency oscillations (HFOs), which potentially serve the purpose for interpreting SPW-R content. However, HFOs have been reported during hippocampal theta, suggesting HFOs serve multiple purposes in interregional communication across different states. In this study, we found that naturally occurring gRSC HFOs occur exclusively in layer 2/3 across all awake brain states. Using focal optogenetic excitation we were able to evoke HFOs in both layer 2/3 and 5. Spontaneous gRSC HFOs occurring without SPW-Rs were highly comodulated with medial entorhinal cortex (MEC) generated high gamma in hippocampal stratum lacunosum moleculare. gRSC HFOs may serve a general role in supporting hippocampo-cortical dialogue during SPW-R and theta brain states to support memory consolidation and encoding, respectively.

The work presented in this thesis aims to provide various tools to be used during design process to make maximum use of the increasing availability of accurate engine blade measurement data for high fidelity fluid mechanic simulations at a reasonable computational expense. A new method for uncertainty propagation for geometric error has been proposed for fluid mechanics codes using adjoint error correction. Inexpensive Monte Carlo (IMC) method targets small uncertainties and provides complete probability distribution for the objective function at a significantly reduced computational cost. A brief literature survey of the existing methods is followed by the formulation of IMC. An example algebraic model is used to demonstrate the IMC method. The IMC method is extended to fluid mechanic applications using Principal Component Analysis (PCA) for reduced order modelling. Implementation details for the IMC method are discussed using an example airfoil code. Finally, the IMC method has been implemented and validated for an industrial fluid mechanic code HYDRA. A consistent methodology has been developed for the automatic generation of the linear and adjoint codes by selective use of automatic differentiation (AD) technique. The method has the advantage of keeping the linear and the adjoint codes in-sync with the changes in the underlying nonlinear fluid mechanic solver. The use of various consistency checks have been demonstrated to ease the development and maintenance process of the linear and the adjoint codes. The use of AD has been extended for the calculation of the complete Hessian using forward-on-forward approach. The complete mathematical formulation for Hessian calculation using the linear and the adjoint solutions has been outlined for fluid mechanic solvers. An efficient implementation for the Hessian calculation is demonstrated using the airfoil code. A new application of the Independent Component Analysis (ICA) is proposed for manufacturing uncertainty source identification. The mathematical formulation is outlined followed by an example application of ICA for artificially generated uncertainty for the NACA0012 airfoil.

L'analyse en composantes indépendantes (ACI) est une méthode d'analyse statistique qui consiste à exprimer les données observées (mélanges de sources) en une transformation linéaire de variables latentes (sources) supposées non gaussiennes et mutuellement indépendantes. Dans certaines applications, on suppose que les mélanges de sources peuvent être groupés de façon à ce que ceux appartenant au même groupe soient fonction des mêmes sources. Ceci implique que les coefficients de chacune des colonnes de la matrice de mélange peuvent être regroupés selon ces mêmes groupes et que tous les coefficients de certains de ces groupes soient nuls. En d'autres mots, on suppose que la matrice de mélange est éparse par groupe. Cette hypothèse facilite l'interprétation et améliore la précision du modèle d'ACI. Dans cette optique, nous proposons de résoudre le problème d'ACI avec une matrice de mélange éparse par groupe à l'aide d'une méthode basée sur le LASSO par groupe adaptatif, lequel pénalise la norme 1 des groupes de coefficients avec des poids adaptatifs. Dans ce mémoire, nous soulignons l'utilité de notre méthode lors d'applications en imagerie cérébrale, plus précisément en imagerie par résonance magnétique. Lors de simulations, nous illustrons par un exemple l'efficacité de notre méthode à réduire vers zéro les groupes de coefficients non-significatifs au sein de la matrice de mélange. Nous montrons aussi que la précision de la méthode proposée est supérieure à celle de l'estimateur du maximum de la vraisemblance pénalisée par le LASSO adaptatif dans le cas où la matrice de mélange est éparse par groupe. / Independent component analysis (ICA) is a method of statistical analysis where the main goal is to express the observed data (mixtures) in a linear transformation of latent variables (sources) believed to be non-Gaussian and mutually independent. In some applications, the mixtures can be grouped so that the mixtures belonging to the same group are function of the same sources. This implies that the coefficients of each column of the mixing matrix can be grouped according to these same groups and that all the coefficients of some of these groups are zero. In other words, we suppose that the mixing matrix is sparse per group. This assumption facilitates the interpretation and improves the accuracy of the ICA model. In this context, we propose to solve the problem of ICA with a sparse group mixing matrix by a method based on the adaptive group LASSO. The latter penalizes the 1-norm of the groups of coefficients with adaptive weights. In this thesis, we point out the utility of our method in applications in brain imaging, specifically in magnetic resonance imaging. Through simulations, we illustrate with an example the effectiveness of our method to reduce to zero the non-significant groups of coefficients within the mixing matrix. We also show that the accuracy of the proposed method is greater than the one of the maximum likelihood estimator with an adaptive LASSO penalization in the case where the mixing matrix is sparse per group.

L'objectif de cette thèse est d'identifier et caractériser les boucles anatomiques et fonctionnelles cortico-sous-corticales chez l'Homme, à partir de données d'imagerie par résonance magnétique fonctionnelle (IRMf) au repos et de diffusion. Une boucle est un ensemble de régions corticales, sous-corticales et cérébelleuses, qui interagissent afin d'effectuer ou de préparer une tâche.Le premier axe de ce travail vise à identifier les réseaux fonctionnels cortico-sous-corticaux en IRMf au repos. Nous proposons une méthode statistique robuste séparant l'analyse corticale de l'analyse sous-corticale. Une analyse en composantes indépendantes spatiales est d'abord réalisée individuellement sur les régions corticales, et suivie d'une classification hiérarchique. Les régions sous-corticales associées sont ensuite extraites par un modèle linéaire général dont les régresseurs comportent la dynamique des régions corticales, suivi d'une analyse de groupe à effets aléatoires. La méthode est validée sur deux jeux de données différents. Un atlas immunohistochimique des structures sous-corticales permet ensuite de déterminer la fonction sensorimotrice, associative ou limbique des réseaux obtenus. Nous montrons enfin que l'anatomie est un support pour la fonction chez des sujets sains.Le dernier axe étudie le syndrome de Gilles de la Tourette, qu'on pense être dû à un dysfonctionnement des boucles cortico-sous-corticales. Nous caractérisons d'abord les boucles cortico-sous-corticales fonctionnelles grâce à des métriques d'intégration et de théorie des graphes, et des différences en termes de connectivité sont mises en évidence entre patients adultes et volontaires sains. Nous montrons également que les boucles cortico-sous-corticales fonctionnelles chez les patients sont soutenues par l'anatomie sous-jacente. / The objective of this thesis is to identify and characterize human anatomical and functional cortico-subcortical loops, using data from resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A loop is a set of cortical, subcortical and cerebellar regions that interact to perform or prepare for a task.We first aim to identify cortico-subcortical functional networks from resting-state fMRI data. We propose a robust statistical method that separates the analysis of cortical regions from that of subcortical structures. A spatial independent component analysis is first performed on individual cortical regions, followed by a hierarchical classification. The associated subcortical regions are then extracted by using a general linear model, the regressors of which contain the dynamics of the cortical regions, followed by a random-effect group analysis. The proposed approach is assessed on two different data sets. An immunohistochemical subcortical atlas is then used to determine the sensorimotor, associative or limbic function of the resulting networks. We finally demonstrate that anatomy is a support for function in healthy subjects.The last part is devoted to the study of the Gilles de la Tourette syndrome, thought to be due to adysfunction of cortico-subcortical loops. Firstly, cortico-subcortical functional loops are characterized using metrics such as integration and graph theory measures, showing differences in terms of connectivity between adult patients and healthy volunteers. Secondly, we show that the cortico-subcortical functional loops in patients are supported by the underlying anatomy.